Abstract
With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.
Highlights
Transportation electrification, such as electric vehicles (EVs), is a clean solution to the replacement of traditional internal combustion engine vehicles for tailpipe emissions reduction [1]
Equivalent circuit model (ECM)-based online State of power (SOP) estimation methods have been rapidly developed in the past decade, with great effort made by manufacturers and researchers in this field, technical challenges are still faced in many aspects
It is believed that the model fusion method is a promising alternative to achieve the improved performance in SOP estimation
Summary
Transportation electrification, such as electric vehicles (EVs), is a clean solution to the replacement of traditional internal combustion engine vehicles for tailpipe emissions reduction [1]. Lithium-ion batteries, as energy storage in EVs, have gained much popularity in industry and academia, owing to their high specific energy and power density, long service life, and light weight [2,3,4] They are inclined to suffer from potential safety issues during service, such as internal short circuit and thermal runaway. They cannot reproduce battery internal dynamics, require a large storage capacity from BMSs, and lack robustness over battery lifetime, all of which become a major obstacle in EV applications.
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